Automated process controllers have been developed to control a substantial number of production processes. However, heretofore these process controllers have had difficulty in adequately stabilizing certain processes when high production rates and/or high product quality are required. In particular, current process controllers are progressively less adequate as the process has more of the following characteristics: inherently unstable, must be exceedingly stable, inherently dangerous when unstable, requires tightly coupled coordination of a substantial number of process actuators, influenced by a number of substantially uncontrollable factors, requires stringent timing and/or substance composition constraints and cannot be effectively continuously directed by a human operator(s). For example, current process controllers such as a commercially available computer driven programmable logic controller (PLC) can monitor and control, in real time, a large number of process actuators and sensors. However, there are at least two noteworthy drawbacks:
(1.1) the process control exhibited by a PLC is low level. That is, there is insufficient coordination among the controllers within the PLC to provide overall process stability. The isolated action of one controller may adversely affect other process reactions, thus resulting in other PLC controllers taking inappropriate responses; PA1 (1.2) a PLC has either insufficient or no means of adapting to or learning the peculiarities of a specific processing facility. For example, such subtle or indirect process affecting factors as wear and degraded performance of process equipment cannot be sufficiently taken into account. PA1 (2.1) to address the difficulties encountered in controlling a process where there is insufficient knowledge explicitly encoded within the process controller of process impacting dynamics or factors for predictably controlling the process; and PA1 (2.2) to substantially separate low level process specific actuator control functionality from more high level process control functions regarding process stability, safety and effectiveness. PA1 (3.1) decision logic directed toward shifting values in the process control data output by the supervising unit and used by the attribute controllers in determining how to direct the process actuators. That is, the decision logic here attempts to shift the process control data values, such as the setpoints and deadbands, for each of one or more attribute controllers such that these values more closely reflect how the process is currently behaving; and PA1 (3.2) decision logic directed toward modifying the execution frequency of one or more attribute controllers. By changing the relative frequency by which one attribute controller executes in comparison to another, destabilizing conflicts between the attribute controllers can be dampened. PA1 (4.1) processing configuration information indicating what pieces of processing equipment are expected to be in use and the substantially static operating characteristics for the equipment. Such configurations can coincide with various states of process facility operation such as start-up, shut-down, maintenance and various process operation states; PA1 (4.2) performance target values that are substantially unchangeable for a given process state and the output product expected. Such values include certain control data target values critical to process control and/or output product target characteristics. Thus, for example, during process operation, there are likely to be target ranges for output product characteristics related to product quality and/or quantity; PA1 (4.3) process control data for controlling the process. This data includes attribute controller setpoints, deadbands, execution frequencies and alarm ranges. Thus, for example, when a process attribute measurement from a process sensor is outside a predetermined deadband range, then a predetermined attribute controller, which governs the process attribute, uses the deadband and/or a related setpoint in determining how to adjust the process so that the process attribute is within the deadband; PA1 (4.4) predetermined process applicability data, i.e., data used for specifying when the data cluster's process control data (4.3) is relevant to controlling the process. Such applicability data includes data related to a number of process factors that influence the process and/or the output product characteristics. In particular, substantially uncontrollable process influencing factors are included. For example, the composition of an input material to the process or the ambient weather conditions can be substantially uncontrollable yet influential for many processes. Thus, for instance, if the composition of the input material to the process can be measured, then the applicability data can include a range of values for the composition of the input material as part of the criteria for determining the applicability of the related process control data of (4.3). PA1 (5.1) whether a learning unit output is likely to generate a "reasonable" submode. That is, whether the submode is likely to cause the processing facility to perform at a minimally acceptable level, and in addition, whether the submode is likely to perform at least as well as any submode it may replace in the mode library; PA1 (5.2) whether the submode's process control data are sufficiently distinct from other submodes, related to the same mode, such that it is worthwhile to include the submode in the mode library.
A particularly challenging process, having substantially all the above characteristics, is the typical coal drying process. Coal drying is a complex process typically using a rapid heat transfer technique to remove water from coal. Commercial coal drying techniques rapidly heat one to four tons of coal to 200.degree.-210.degree. F. using combustion exhaust gas to vaporize any water residing in the coal. In controlling such a real time process involving an energy transfer to a substance (e.g., coal) for changing a property of the substance, it is desirable to stabilize the process such that there are not substantial oscillations in the reactions within the process. By stabilizing the process appropriately, a consistently high quality, cost effective product can be obtained. Alternatively, substantial instability can result in significant damage to both property and personnel. For example, the optimal free oxygen gas content of a coal drying chamber is approximately 4%. However, if the free oxygen gas content exceeds 7%, the coal being dried is likely to explode. Further, under certain conditions, seemingly small fluctuations in process control parameters can cause exhaust gas emissions to exceed environmental regulations. Thus, even small variations in the content of the coal drying gas must be detected and acted upon quickly and properly.
It is known to utilize a high level process control supervisor procedure to control a PLC. In U.S. Pat. No. 5,006,992, issued to Richard D. Skeirik, a process control system is disclosed having a high level supervisor procedure that is modular such that the modules can be easily revised without significant interruption in the operation of the process supervisor. The supervisor can also call on various expert subsystems to which it is linked to provide expert knowledge in controlling various aspects of the process.
It is also known to use a learning system for diagnosing and/or controlling a process. Such features are disclosed in U.S. Pat. No. 4,730,259 and U.S. Pat. No. 5,089,963.
In the '259 patent, issued to Stephen I. Gallant, an expert system architecture is disclosed for use in diagnostic and/or process control systems. The expert system includes an inference engine which uses a "matrix of learning coefficients" (i.e., a neural net) as the knowledge base representation from which inferences are made. Further, the matrix can be generated from training examples and, optionally, user inserted rules. The matrix can also be dynamically modified as the system operates. The expert system is embodied in the context of a simple disease diagnosis example. No further embodiments are illustrated.
In the '963 patent, issued to Hiroshi Takahashi, an automobile transmission gear shifting control system is disclosed which employs a learning device similar to a neural net for learning how to select one of a predetermined number of gear shifting strategies according to sensor inputs related to driving conditions and the driver's transmission gear shifting pattern preferences. Once sufficient learning has taken place, the control system can subsequently automatically recall and activate the preferred shifting strategy on the basis of the monitored driving parameters and the stored driving patterns.
Regardless, however, of the above prior art and other such process control methods and apparatuses, there is a need for a process controller and process controller architecture for safely and cost effectively stabilizing such difficult processes as coal drying. In particular, it would be advantageous to have such a process controller which provides real time control over the entire process during product processing, substantially without the need for operator control. Moreover, there should be virtually no circumstance where an operator is required to make a snap judgment decision forced by process time critical constraints. Thus, such a novel process controller should be able to safely, automatically and in real time coordinate the adjustment of process control settings in response to sensor input data and automatically execute alternative process control strategies, including emergency shut-downs, whenever the current strategy is deemed ineffective. Moreover, the process controller should be capable of continually adapting or learning how process equipment and environmental factors affect the process and the resulting product. Thus, when there is a large number of process related factors that are related to one another and to the characteristics (i.e., quality and/or quantity) of the resulting product in complex, variable and oftentimes processing site specific ways, the process controller should be capable of continually adapting to such relationships in a safe and cost effective manner.